F_VN_PredictSampleVectorExp

Compute a vectorial prediction for a single sample. (expert function)
Can use available TwinCAT Job Tasks for executing parallel code regions.

Syntax

Definition:

FUNCTION F_VN_PredictSampleVectorExp : HRESULT
VAR_INPUT
    ipRegressor  : ITcVnMlModel;
    ipSample     : ITcUnknown;
    ipPrediction : Reference To  ITcVnContainer;
    fNovelty     : Reference To  REAL;
    hrPrev       : HRESULT;
END_VAR

Inputs

Name	Type	Description
ipRegressor	ITcVnMlModel	Regressor to be used
ipSample	ITcUnknown	Container holding a single input sample (ContainerType_Vector_REAL or ContainerType_Vector_LREAL)
ipPrediction	Reference To ITcVnContainer	Returns the predicted output (ContainerType_Vector_REAL or ContainerType_Vector_LREAL, depending on ipSample)
fNovelty	Reference To REAL	Returns the degree of novelty (0.0 if a sample is completely known; > 0.0 otherwise) of the presented sample (optional, set to 0 if not required)
hrPrev	HRESULT	HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.)

Return value

HRESULT

Further information

The function F_VN_PredictSampleVectorExp is the expert variant of F_VN_PredictSampleVector. It contains additional parameters.

Parameter

Regression model

The previously trained regression model must be transferred to ipRegressor.

Sample

The sample container is transferred as ipSample. The container type must be either ContainerType_Vector_REAL or ContainerType_Vector_LREAL.

Prediction

The calculated prediction vector is returned as a container via the reference ipPrediction. The container type is taken from ipSamples.

Degree of novelty

The degree of novelty of the sample is returned via fNovelty.

Application

For example, the prediction vector of a sample can be calculated as follows:

hr := F_VN_PredictSampleVectorExp(
    ipRegressor := ipRegressor,
    ipSample    := ipSample,
    ipPrediction:= ipPrediction,
    fNovelty    := fNovelty,
    hrPrev      := hr);

Related functions

Required License

TC3 Vision Machine Learning

System Requirements

Development environment	Target platform	PLC libraries to include
TwinCAT V3.1.4024.54 or later	PC or CX (x64) with PL50, e.g. Intel 4-core Atom CPU	Tc3_Vision